Performance based rank prediction for MIMO design

ABSTRACT

The performance of a Single Code Word (SCW) design with low complexity MMSE receiver &amp; rank prediction is similar to the Multiple Code Word (MCW) design with successive interference cancellation (SIC). A method of rank prediction comprises calculating MIMO channel matrices corresponding to layer transmissions for each tone, calculating signal-to-noise ratios (SNRs) for each tone based on the MIMO channel matrices, mapping the SNR for each tone to generate effective SNRs for each layer transmission, selecting a highest packet format (PF) with an SNR threshold less than the effective SNR for each layer transmission, maximizing an over-all spectral efficiency based on the selected highest packet formats for each layer transmission, and selecting a rank based on maximizing an over-all spectral efficiency.

REFERENCE TO CO-PENDING APPLICATIONS FOR PATENT

The present Application for Patent is related to the followingco-pending U.S. patent application: “Capacity Based Rank Prediction forMIMO Design,” filed Dec. 22, 2004, U.S. Pat. No. 6,636,568 entitled“Data Transmission with Non-Uniform Distribution of Data Rates for aMultiple-Input Multiple-Output (MIMO) System” and U.S. ProvisionalApplication No. 60/590,113 , filed Jul. 21, 2004 entitled “Efficient CQISignaling Over Access Channel” are assigned to the assignee hereof, andexpressly incorporated by reference herein.

BACKGROUND

I. Field

The present invention relates generally to communications, and morespecifically to techniques for determining a distribution of a datastream to be transmitted via a multi-channel, e.g., a multiple-inputmultiple-output (MIMO), orthogonal frequency division multiplexing(OFDM) communication system.

II. Background

In a wireless communication system, an RF modulated signal from atransmitter may reach a receiver via a number of propagation paths. Thecharacteristics of the propagation paths typically vary over time due toa number of factors such as fading and multipath. To provide diversityagainst deleterious path effects and improve performance, multipletransmit and receive antennas may be used. If the propagation pathsbetween the transmit and receive antennas are linearly independent(i.e., a transmission on one path is not formed as a linear combinationof the transmissions on the other paths), which is generally true to atleast an extent, then the likelihood of correctly receiving a datatransmission increases as the number of antennas increases. Generally,diversity increases and performance improves as the number of transmitand receive antennas increases.

A multiple-input multiple-output (MIMO) communication system employsmultiple (N_(T)) transmit antennas and multiple (N_(R)) receive antennasfor data transmission. A MIMO channel formed by the N_(T) transmit andN_(R) receive antennas may be decomposed into N_(S) independentchannels, with N_(S)≦min {N_(T), N_(R)}. Each of the N_(S) independentchannels may also be referred to as a spatial subchannel (or atransmission channel) of the MIMO channel and corresponds to adimension. The MIMO system can provide improved performance (e.g.,increased transmission capacity) if the additional dimensionalitiescreated by the multiple transmit and receive antennas are utilized.

For a full-rank MIMO channel, where N_(S)=N_(T)≦N_(R), an independentdata stream may be transmitted from each of the N_(T) transmit antennas.The transmitted data streams may experience different channel conditions(e.g., different fading and multipath effects) and may achieve differentsignal-to-noise-and-interference ratios (SNRs) for a given amount oftransmit power. Moreover, if successive interference cancellationprocessing is used at the receiver to recover the transmitted datastreams (described below), then different SNRs may be achieved for thedata streams depending on the specific order in which the data streamsare recovered. Consequently, different data rates may be supported bydifferent data streams, depending on their achieved SNRs. Since thechannel conditions typically vary with time, the data rate supported byeach data stream also varies with time.

The MIMO design has two modes of operation—the single code word (SCW)and multiple-code word (MCW).

In MCW mode, the transmitter can encode the data transmitted on eachspatial layer independently, possibly with different rates. The receiveremploys a successive interference cancellation (SIC) algorithm whichworks as follows: Decode the first layer, and then subtract itscontribution from the received signal after re-encoding and multiplyingthe encoded first layer with an “estimated channel,” then decode thesecond layer and so on. This “onion-peeling” approach means that eachsuccessively decoded layer sees increasing signal-to-noise (SNR) andhence can support higher rates. In the absence of error-propagation, MCWdesign with SIC achieves capacity. The disadvantage of this design arisefrom the burden of “managing” the rates of each spatial later—(a)increased CQI feedback (one CQI for each layer); (b) increased ACK/NACKmessaging (one for each layer); (c) complications in Hybrid ARQ (HARQ)since each layer can terminate at different transmissions; (d)performance sensitivity of SIC to channel estimation errors withincreased Doppler, and/or low SNR; and (e) Increased decoding latencyrequirements since each successive layer cannot be decoded until priorlayers are decoded.

In the conventional SCW mode design, the transmitter encodes the datatransmitted on each spatial layer with “identical data rates.” Thereceiver can employ a low complexity linear receiver such as a MinimumMean Square Solution (MMSE) or Zero Frequency (ZF) receiver, ornon-linear receivers such as QRM, for each tone.

The SCW design overcomes the above mentioned implementation hassles ofthe MCW design. The drawback is that the SCW mode cannot support the MCWrates in spatially correlated channels or line-of-sight (LOS) channelswith a high K-factor. Both of these scenarios lead to a loss in channelrank or increase in channel condition number and increased inter-layerinterference. This dramatically lowers the effective SNR for eachspatial layer. Hence, the data rate supported by each layer is lowered,which lowers the overall data rate.

K-factor is the ratio of the LOS channel power to the non-LOS channelpower. Rank is the number of eigen-modes in the channel with non-zeroenergy. Condition Number is the ratio of the largest eigenvalue to thesmallest eigen-value of the MIMO channel.

There is therefore a need in the art for techniques to distribute a datastream dynamically to be transmitted via a multi-channel, e.g., amultiple-input multiple-output (MIMO), orthogonal frequency divisionmultiplexing (OFDM) communication system.

SUMMARY

In an aspect, a method of rank prediction comprises calculating MIMOchannel matrices corresponding to transmissions with each possiblemultiplexing order for each tone, calculating signal-to-noise ratios(SNRs) for each tone based on the MIMO channel matrices, mapping the SNRfor each tone to generate effective SNRs for each possible multiplexingorder, selecting a highest packet format (PF) with an SNR threshold lessthan the effective SNR for each layer transmission, selecting anabsolute highest PF of the selected highest PFs for each layertransmission, and selecting a rank based on the selected absolutehighest PF.

In another aspect, a wireless communications device comprises means forcalculating MIMO channel matrices corresponding to layer transmissionsfor each tone, means for calculating signal-to-noise ratios (SNRs) foreach tone based on the MIMO channel matrices, means for mapping the SNRfor each tone to generate effective SNRs for each layer transmission,means for selecting a highest packet format (PF) with an SNR thresholdless than the effective SNR for each layer transmission, means forselecting an absolute highest PF of the selected highest PFs for eachlayer transmission, and means for selecting a rank based on the selectedabsolute highest PF.

In another aspect, a processor programmed to execute a method of rankprediction, the method comprises calculating MIMO channel matricescorresponding to layer transmissions for each tone, calculatingsignal-to-noise ratios (SNRs) for each tone based on the MIMO channelmatrices, mapping the SNR for each tone to generate effective SNRs foreach layer transmission, selecting a highest packet format (PF) with anSNR threshold less than the effective SNR for each layer transmission,selecting an absolute highest PF of the selected highest PFs for eachlayer transmission, and selecting a rank based on the selected absolutehighest PF.

In yet another aspect, a computer readable media embodying a method ofrank prediction, the method comprises calculating MIMO channel matricescorresponding to layer transmissions for each tone, calculatingsignal-to-noise ratios (SNRs) for each tone based on the MIMO channelmatrices, mapping the SNR for each tone to generate effective SNRs foreach layer transmission, selecting a highest packet format (PF) with anSNR threshold less than the effective SNR for each layer transmission,selecting an absolute highest PF of the selected highest PFs for eachlayer transmission, and selecting a rank based on the selected absolutehighest PF.

Various aspects and embodiments of the invention are described infurther detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and nature of the present invention will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings in which like reference charactersidentify correspondingly throughout and wherein:

FIG. 1 shows a conventional SCW transmitter;

FIG. 2 shows an SCW transmitter with rank prediction in accordance withan embodiment;

FIG. 3 shows circular multiplexing with M_(T)=4, M=2, B=1 in accordancewith an embodiment;

FIG. 4 shows block-circular multiplexing with M_(T)=4, M=2, B=4 inaccordance with an embodiment; and

FIG. 5 shows a block diagram for performance based rank prediction inaccordance with an embodiment.

DETAILED DESCRIPTION

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs.

The techniques described herein for performance based rank predictionmay be used for various communication systems such as a Code DivisionMultiple Access (CDMA) system, a Wideband CDMA (WCDMA) system, a directsequence CDMA (DS-CDMA) system, a Time Division Multiple Access (TDMA)system, a Frequency Division Multiple Access (FDMA) system, a High SpeedDownlink Packet Access (HSDPA) system, an orthogonal frequency divisionmultiplexing (OFDM)-based system, an Orthogonal Frequency DivisionMultiple Access (OFDMA) system, a single-input single-output (SISO)system, a multiple-input multiple-output (MIMO) system, and so on.

OFDM is a multi-carrier modulation technique that effectively partitionsthe overall system bandwidth into multiple (NF) orthogonal subbands.These subbands are also referred to as tones, subcarriers, bins, andfrequency channels. With OFDM, each subband is associated with arespective subcarrier that may be modulated with data. Up to NFmodulation symbols may be transmitted on the NF subbands in each OFDMsymbol period. Prior to transmission, these modulation symbols aretransformed to the time-domain using an NF-point inverse fast Fouriertransform (IFFT) to obtain a “transformed” symbol that contains NFchips.

The SCW design overcomes the drawbacks of MCW design. However, SCW modecannot support the MCW rates in spatially correlated channels orline-of-sight (LOS) channels with a high K-factor. Both of thesescenarios lead to a loss in channel rank or increase in channelcondition number and increased inter-layer interference. Thisdramatically lowers the effective SNR for each spatial layer. Hence, thedata rate supported by each layer is lowered, which lowers the overalldata rate.

One way to reduce interlayer interference is to lower the number ofspatial layers transmitted in low-rank channels, and trade-offinter-layer interference and MIMO gains. For example, lowering thenumber of layers transmitted from four to three, i.e., decreasing therank from four to three, can dramatically increase the effective SNRsfor the three layers and hence the data rate supported by each layer.The net-effect is that a three-layer transmission can in-fact have ahigher spectral efficiency compared to a four-layer transmission.

In an embodiment, the SCW design effectively trades off the interlayerinterference and MIMO gains to maximize overall spectral efficiency.This is achieved via rank prediction, where the receiver feeds back anoptimal number of layers for transmission in addition to aCarrier-Quality-to-Interference (CQI) to match the channel rank.

It would be apparent to those skilled in the art that quality indicatorsother than CQI may be utilized.

Conventional SCW Transmitter

FIG. 1 shows a conventional SCW transmitter 100. The bits 102 areturbo-encoded 104 and QAM mapped 106 depending on the packet format (PF)108, 110, specified by a rate prediction algorithm 112. The encoding isidentical to a single-in-single-out (SISO) design. The coded symbols arethen de-multiplexed 114 to M_(T) layers 116, which are then spatiallymapped 118 to M_(T) OFDM modulators 120 and antennas 122. The OFDMprocessing for each transmit antenna proceeds then in an identicalfashion as the SISO, after which the signals are launched into a MIMOwireless channel. The rate prediction algorithm uses a 4-bit CQIfeedback 124 from receiver 126 every 5 msec. The CQI is a measure of theeffective SNR/spatial layer, measured at the receiver. The spatialmapping is done in manner to ensure that the SNR for each layer aresimilar. As explained before, the performance of this design suffers inlow rank channels.

SCW Transmitter with Rank Prediction

In accordance with an embodiment, a single code word (SCW) design withrank prediction is described. Algorithms for robust rank prediction arepresented below. For SNR<15 dB (90% of the users), the performance ofthe SCW design with low complexity MMSE receiver & rank prediction, issimilar to the Multiple Code Word (MCW) design with successiveinterference cancellation (SIC). Without HARQ, SCW is better than MCWsince MCQ is more sensitive to channel estimation errors. These factorsmake SCW attractive for MIMO due to smaller implementation complexityand overhead compared to MCW.

For SNR between 15 and 20 dB (10% of the users), the performance gapbetween SCW and MCW is less than 1.0 dB for low K channels, and 2-5 dBfor high K channels. For high K channels, the performance degradation athigh SNRs is lowered to 1-2 dB, by employing dual polarized antennas. Ineffect, the SCW design is within two dB of MCW design even at high SNRs.In the absence of HARQ, the performance of MCW is worse than SCW atSNR<15 dB, due to increased sensitivity of SIC to channel estimationerrors.

FIG. 2 shows an SCW transmitter with rank prediction in accordance withan embodiment. The bits 202 are turbo-encoded 204 and QAM mapped 206depending on the packet format (PF) 208, 210, specified by a rateprediction algorithm 212.

In an embodiment, the coded symbols are then de-multiplexed 214 to Mstreams 216 or layers (1≦M≦M_(T)), where M 228 is a 2-bit integer1≦M≦M_(T) specified by the receiver 226 feedback every 5 m-sec, inaddition to a 5-bit CQI 224. The M streams 216 are then spatially mapped218 to M_(T) OFDM modulators 220 and M_(T) antennas 222.

Spatial Mapping

The spatial mapper (precoder) 218 is a M_(T)×M matrix P(k) that maps Msymbols on to M_(T) antennas, for each OFDM tone, k. There can beseveral choices for the precoder. Consider a M_(R)×M_(T) MIMO channelH(k). The precoder matrices can be chosen so that the equivalent channelmatrix H(k)P(k) has improved frequency selectivity compared to H(k). Theincreased frequency selectivity can be exploited by the decoder toobtain frequency diversity gains.

In an embodiment, a precoder matrix is the following permutation matrix:

${P_{M}(k)} = {\frac{1}{\sqrt{M}}{\Pi\left\lbrack {\left\lfloor \frac{k}{B} \right\rfloor{mod}\; M_{T}} \right\rbrack}}$where Π(0), Π(1), . . . , Π(M_(T)−1) are the M_(T)×M sub-permutationmatrices derived from the M columns of the identity matrix, I_(M) _(T)_(×M) _(T) and B is a parameter to control the frequency selectivity ofthe equivalent channel.

In accordance with an embodiment, if M_(T)=4, M=2, then

${\Pi\lbrack 0\rbrack} = {{\begin{bmatrix}1 & 0 \\0 & 1 \\0 & 0 \\0 & 0\end{bmatrix}\mspace{14mu}{\Pi\lbrack 1\rbrack}} = {{\begin{bmatrix}0 & 0 \\1 & 0 \\0 & 1 \\0 & 0\end{bmatrix}\mspace{14mu}{\Pi\lbrack 2\rbrack}} = {{\begin{bmatrix}0 & 0 \\0 & 0 \\1 & 0 \\0 & 1\end{bmatrix}\mspace{14mu}{\Pi\lbrack 3\rbrack}} = \begin{bmatrix}0 & 1 \\0 & 0 \\0 & 0 \\1 & 0\end{bmatrix}}}}$

For B=1, this leads to a circular multiplexing strategy with two layersas shown in FIG. 3 wherein the vertical-lined boxes 302 correspond tosymbols from layer one and horizontal-lined boxes 304 correspond tosymbols from layer 2. FIG. 3 shows circular multiplexing with M_(T)=4,M=2, B=1. The vertical axis 306 represents antennas. The horizontal axis308 represents tones.

For B=4, this leads to a block-circular multiplexing strategy with twolayers as shown in FIG. 4 where the vertical-lined boxes 402 correspondto symbols from layer one and horizontal-lined boxes 404 correspond tosymbols from layer 2. FIG. 4 shows block-circular multiplexing withM_(T)=4, M=2, B=4. The vertical axis 406 represents antennas. Thehorizontal axis 408 represents tones.

An increase in B leads to a reduction in the frequency selectivity ofthe equivalent channel, which may be desirable when weak codes areemployed. Also, the parameter B is sensitive to channel interleaverchoice, therefore parameter B may be optimized later on.

Circular multiplexing improves frequency diversity regardless of thechannel delay spread. In the presence of strong turbo codes, theperformance of CM (with M=1) approaches Space-Time transmit diversity(STTD). However, for very high PFs or for control channels that employweak convolutional codes, STTD can out-perform CM significantly.

In an embodiment, a precoder matrix is the following generalized delaydiversity matrix:

${P_{M}(k)} = {\frac{1}{\sqrt{M}}\Delta_{M_{T} \times M_{T}}\Theta_{M_{T} \times M}}$

where ΘM_(T)×M is a M_(T)×M sub-DFT matrix obtained from the M columnsof the M_(T)×M_(T) DFT matrix, and Δ_(M) _(T) _(×M) _(T) is anM_(T)×M_(T) diagonal matrix, with the (j,j)^(th) entry given by

${\exp\left\lbrack \frac{j\; 2\;{\pi\left( {k - 1} \right)}\delta}{N} \right\rbrack}.$

The parameter δ is the delay-parameter, which also controls thefrequency selectivity of the channel, and N is the number of OFDM tones.We note that for M=1, the above precoding matrix implements the “pure”delay diversity. The performance of delay diversity is strictly worsethan circular multiplexing (and STTD), and has poor performance in LOSchannel conditions for high PF. The only advantage of using delaydiversity is that it benefits from improved SISO channel estimationgains at very low SNRs (SNR<−5 dB) and for high mobility (>120 kmph). Inthese channel scenarios, circular multiplexing cannot benefit from SISOchannel estimation gains.

Packet Formats

A current SISO design uses 7 PFs with spectral efficiencies [0.5, 1,1.5, 2.0, 2.5, 3.0, 4.0] bps/Hz. In the SCW design employing a one-layertransmission, this granularity in spectral efficiency (SE) should besufficient. However, when all four layers are used for transmission,this translates to spectral efficiencies of [2, 4, 6, 8, 10, 12, 16]bps/Hz, with a SE granularity on the order of 2-4 bps/Hz. A consequenceof this coarse granularity is a loss in data rate, since these users areconstrained to transmit at a much lower data rate than their attainableSE. Note that MCW design with SIC does not have this granularityproblem, since the rate in each layer can be adjusted independently,resulting in an overall finer spectral efficiency granularity.

TABLE 1 Packet Format for SCW Design with Rank Prediction Code RateSpectral Efficiency Per Layer Packet Modu- after one after N frames oftransmission Format lation Frame 1 2 3 4 5 6 0 2 1/4 0.50 0.25 0.17 0.130.10 0.08 1 2 3/8 0.75 0.38 0.25 0.19 0.15 0.13 2 2 1/2 1.00 0.50 0.330.25 0.20 0.17 3 4  5/16 1.25 0.63 0.42 0.31 0.25 0.21 4 4 3/8 1.50 0.750.50 0.38 0.30 0.25 5 4  7/16 1.75 0.88 0.58 0.44 0.35 0.29 6 4 1/2 2.001.00 0.67 0.50 0.40 0.33 7 4  9/16 2.25 1.13 0.75 0.56 0.45 0.38 8 6 5/12 2.50 1.25 0.83 0.63 0.50 0.42 9 6 11/24 2.75 1.38 0.92 0.69 0.550.46 10 6 1/2 3.00 1.50 1.00 0.75 0.60 0.50 11 6 13/24 3.25 1.63 1.080.81 0.65 0.54 12 6  7/12 3.50 1.75 1.17 0.88 0.70 0.58 13 6 5/8 3.751.88 1.25 0.94 0.75 0.63 14 6 2/3 4.00 2.00 1.33 1.00 0.80 0.67 15 617/24 4.25 2.13 1.42 1.06 0.85 0.71

Table 1 shows the packet format for SCW design with rank prediction inaccordance with an embodiment. Table 1 shows the PFs with SEs targetingthe first to transmission. 16 PFs are provisioned with SE-per-layerranging from 0.5 bps/Hz/layer to 4.25 bps/Hz/layer with 0.25bps/Hz/layer increments targeting the first transmission. When targetingthe third transmission, the maximum attainable SE-per-layer is 1.42bps/Hz/layer. The SE between 1.42 bps/Hz/layer and 2.13 bps/Hz/layer canbe achieved by targeting the second transmission and SE greater than2.13 bps/Hz/layer can be achieved by targeting the first transmission,where HARQ benefits diminish.

In another embodiment, more PF#s may be added with SE/layer >4.25 bps/Hzso that higher SE can be achieved by targeting the third transmission,and benefit from HARQ gains. In such a case, a 6-bit CQI may be neededto ensure that the PF granularity is captured.

Performance Based Rank Prediction Algorithm

FIG. 5 shows a block diagram for performance based rank prediction inaccordance with an embodiment. For the kth tone, H(k)P₁(k) 502 throughH(k)P₄(k) 508 are input into MMSE(1) 512 through MMSE(4) 518,respectively. MMSE(1) 512 through MMSE(4) 518 produce SNR₁(k) 522through SNR₄(k) 528, respectively. SNR₁(k) 522 through SNR₄(k) 528 areinput into Cap Mapper 532 through Cap Mapper 538, respectively. CapMapper 532 through Cap Mapper 538 produce EffSNR₁ 542 through EffSNR₄548, respectively. EffSNR₁ 542 through EffSNR₄ 548 are input into PFSelect 552 through PF Select 558, respectively. SNR thresholds at a 1%packet error rate (PER) are input into PF Select 552 through PF Select558. PF Select 552 through PF Select 558 produce 1×PF1 562 through 4×PF4568, respectively. 1×PF1 562 through 4×PF4 568 are input into a decisionunit 570. Decision unit 570 produces a rank 572.

EffSNR₁ 542 through EffSNR₄ 548 and the rank 572 are input into a select& quantize unit 574. The select & quantize unit 574 produces a five-bitCQI 576.

In accordance with an embodiment, the performance based rank predictionalgorithm works as follows:

1. At each tone, the 4×4, 4×3, 4×2 and 4×1 MIMO channel matrices,H(k)P₁(k), H(k)P₂(k), H(k)P₃(k) and H(k)P₄(k), corresponding to the {1,2, 3, 4} layer transmissions, are calculated. Assuming an MMSE receiver,the post-processing SNRs for {1, 2, 3, 4} layer transmissions, SNR₁(k),SNR₂(k), SNR₃(k), SNR₄(k) are calculated for each tone as:

${{{SNR}_{M}(k)} \approx {\frac{1}{M}{\sum\limits_{m = 0}^{M - 1}\;{\left\lbrack {{diag}\left\langle \left\lbrack {{{P_{M}(k)}*{H(k)}*{H(k)}{P_{M}(k)}} + {\sigma^{2}I}} \right\rbrack^{- 1} \right\rangle} \right\rbrack_{m,m}^{- 1}{\forall M}}}}} = \left\lbrack {1,4} \right\rbrack$If we assume other receivers such as QRM-MLD or IDD, the post processingSNRs will be calculated in a different fashion.

2. The SNRs calculated above for the {1, 2, 3, 4} layer transmissions,are equivalent to the per-tone receiver SNRs calculated for the SISOdesign. An unconstrained-capacity mapping is then employed (as in theSISO design) to generate an effective-SNR averaged over all tones, forthe {1, 2, 3, 4} layer transmissions, which are denoted as EffSNR₁,EffSNR₂, EffSNR₃, EffSNR₄. There is no tone-dependency for the effectiveSNRs.

3. The effective SNRs are compared against a table with SNR thresholdstargeting the 1% PER for a SISO system. The highest packet format (PF)with SNR threshold less than the measured effective SNR are selected forthe {1, 2, 3, 4} layer transmissions. The PFs are denoted as PF₁, PF₂,PF₃, PF₄.

4. The optimum rank/layer is chosen so as to maximize the over-allspectral efficiency, i.e.,

$\hat{M} = {{\underset{M = {\lbrack{1,4}\rbrack}}{\arg\mspace{14mu}\max}\left\lbrack {M \times {PF}_{M}} \right\rbrack}.}$

5. A 5-bit CQI is then fed-back, where CQI({circumflex over (M)})=Quant[EffSNR_({circumflex over (M)})].

The techniques described herein may be used for a variety of OFDM-basedsystems as well as other systems. The rank prediction techniquesdescribed herein may be implemented by various means. For example, thesetechniques may be implemented in hardware, software, or a combinationthereof. For a hardware implementation, the processing units used toperform interference control may be implemented within one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof.

For a software implementation, the interference control techniques maybe implemented with modules (e.g., procedures, functions, and so on)that perform the functions described herein. The software codes may bestored in a memory unit and executed by a processor. The memory unit maybe implemented within the processor or external to the processor, inwhich case it can be communicatively coupled to the processor viavarious means as is known in the art.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A method of rank prediction, comprising:calculating MIMO channel matrices corresponding to layer transmissionsfor each tone; calculating signal-to-noise ratios (SNRs) for each tonebased on the MIMO channel matrices; mapping the SNRs for each tone togenerate effective SNRs for each layer transmission; selecting a highestpacket format (PF) with an SNR threshold less than the effective SNRsfor each layer transmission; selecting an absolute highest PF of theselected highest PF for each layer transmission; selecting a rank basedon the selected absolute highest PF; and transmitting a ranking to atransmitting side which indicates the layer transmissions to select fortransmission so as to maximize spectral efficiency.
 2. The method ofclaim 1, further, comprising sending a quality indicator based on theselected rank.
 3. The method of claim 2, wherein the quality indicatoris Carrier-Quality-to-Interference (CQI).
 4. The method of claim 3,wherein the quality indicator CQI is calculated as CQI({circumflex over(M)})=Quant [EffSNR_({circumflex over (M)})], where EffSNR is theeffective SNRs of the selected rank.
 5. The method of claim 1, whereinthe number of the layer transmissions is four.
 6. The method of claim 1,wherein the SNR for each tone is calculated as${{{{SNR}_{M}(k)} \approx {\frac{1}{M}{\sum\limits_{m = 0}^{M - 1}\;{\left\lbrack {{diag}\left\langle \left\lbrack {{{P_{M}(k)}*{H(k)}*{H(k)}{P_{M}(k)}} + {\sigma^{2}I}} \right\rbrack^{- 1} \right\rangle} \right\rbrack_{m,m}^{- 1}{\forall M}}}}} = \left\lbrack {1,4} \right\rbrack},$where k is the kth tone, and H(k)P₁(k), H(k)P₂(k), H(k)P₃(k), andH(k)P₄(k), correspond to {1, 2, 3, 4} layer transmissions.
 7. The methodof claim 1, wherein the mapping is unconstrained with respect tocapacity.
 8. The method of claim 1, wherein the selected rank{circumflex over (M)} is calculated as$\hat{M} = {{\underset{M = {\lbrack{1,4}\rbrack}}{\arg{\mspace{11mu}\;}\max}\left\lbrack {M \times {PF}_{M}} \right\rbrack}.}$9. A wireless communications device, comprising: means for calculatingMIMO channel matrices corresponding to layer transmissions for eachtone; means for calculating signal-to-noise ratios (SNRs) for each tonebased on the MIMO channel matrices; means for mapping the SNRs for eachtone to generate effective SNRs for each layer transmission; means forselecting a highest packet format (PF) with an SNR threshold less thanthe effective SNRs for each layer transmission; means for selecting anabsolute highest PF of the selected highest PF for each layertransmission; means for selecting a rank based on the selected absolutehighest PF; and means for transmitting a ranking to a transmitting sidewhich indicates the layer transmissions to select for transmission so asto maximize spectral efficiency.
 10. The wireless communications deviceof claim 9, further comprising means for sending a quality indicatorbased on the selected rank.
 11. The wireless communications device ofclaim 10, wherein the quality indicator isCarrier-Quality-to-Interference.
 12. The wireless communications deviceof claim 9, wherein the number of the layer transmissions is at leasttwo.
 13. A processor programmed to execute a non-transitorycomputer-readable medium of a method of rank prediction to maximizespectral efficiency in a MIMO wireless communication system, the methodcomprising: calculating MIMO channel matrices corresponding to layertransmissions for each tone; calculating signal-to-noise ratios (SNRs)for each tone based on the MIMO channel matrices; mapping the SNRs foreach tone to generate effective SNRs for each layer transmission;selecting a highest packet format (PF) with an SNRs threshold less thanthe effective SNRs for each layer transmission; selecting an absolutehighest PF of the selected highest PF for each layer transmission;selecting a rank based on the selected absolute highest PF; andtransmitting a ranking to a transmitting side which indicates the layertransmissions to select for transmission so as to maximize spectralefficiency.
 14. The processor of claim 13, wherein the method furthercomprises sending a quality indicator based on the selected rank. 15.The processor of claim 14, wherein the quality indicator isCarrier-Quality-to-Interference.
 16. The processor of claim 13, whereinthe number of the layer transmissions is at least two.
 17. Anon-transitory computer-readable medium embodying instructionsexecutable by a processor for providing a method of rank prediction tomaximize spectral efficiency in a MIMO wireless communication system,the method comprising: calculating MINO channel matrices correspondingto layer transmissions for each tone; calculating signal-to-noise ratios(SNRs) for each tone based on the MIMO channel matrices; mapping theSNRs for each tone to generate effective SNRs for each layertransmission; selecting a highest packet format (PF) with an SNRthreshold less than the effective SNRs for each layer transmission;selecting an absolute highest PF of the selected highest PF for eachlayer transmission; selecting a rank based on the selected absolutehighest PF; and transmitting a ranking to a transmitting side whichindicates the layer transmissions to select for transmission so as tomaximize spectral efficiency.
 18. The computer-readable medium of claim17, wherein the method further comprises sending a quality indicatorbased on the selected rank.
 19. The computer readable of claim 18,wherein the quality indicator is Carrier-Quality-to-Interference. 20.The computer readable of claim 17, wherein the number of the layertransmissions is at least two.
 21. An apparatus for performing rankprediction, comprising: a plurality of receiving circuits for receivingcalculated MIMO channel matrices corresponding to layer transmissionsfor each tone and calculating signal-to-noise ratios (SNRs) for eachtone based on the MIMO channel matrices; a plurality of capacitymappers, coupled to the plurality of receivers, for mapping the SNRs foreach tone to generate effective SNRs for each layer transmission; atleast one packet format (PF) selector, coupled to the plurality ofcapacity mappers, for selecting a highest PF with an SNR threshold lessthan the effective SNRs for each layer transmission; and a decisionunit, coupled to the at least one PF selector, for selecting an absolutehighest PF of the selected highest PF for each layer transmission,selecting a rank based on the selected absolute highest PF, andoutputting the rank for forwarding to a transmitting side whichindicates the layer transmissions to select for transmission so as tomaximize spectral efficiency.
 22. The apparatus of claim 21, furthercomprising a select-and-quantize unit, coupled to the plurality ofcapacity mappers, for generating and sending a quality indicator basedon the selected rank.
 23. The apparatus of claim 22, wherein the qualityindicator is Carrier-Quality-to-Interference (CQI).
 24. The apparatus ofclaim 23, wherein the CQI is calculated as CQI({circumflex over(M)})=Quant[EffSNR_({circumflex over (M)})], where EffSNR is theeffective SNRs of the selected rank.
 25. The apparatus of claim 21,wherein the number of the layer transmissions is four.
 26. The apparatusof claim 21, wherein the SNR for each tone is calculated as${{{{SNR}_{M}(k)} \approx {\frac{1}{M}{\sum\limits_{m = 0}^{M - 1}{\left\lbrack {{diag}\left\langle \left\lbrack {{{P_{M}(k)}^{*}{H(k)}^{*}{H(k)}{P_{M}(k)}} + {\sigma^{2}I}} \right\rbrack^{- 1} \right\rangle} \right\rbrack_{m,m}^{- 1}{\forall M}}}}} = \left\lbrack {1, 4} \right\rbrack},$where k is the kth tone, and H(k)P₁(k), H(k)P₂(k), H(k)P₃(k) andH(k)P₄(k) correspond to {1,2,3,4} layer transmissions.
 27. The apparatusof claim 21, wherein the mapping is unconstrained with respect tocapacity.
 28. The apparatus of claim 21, wherein the selected rank{circumflex over (M)} is calculated as$\hat{M} = {{\underset{M = {\lbrack{1,4}\rbrack}}{\arg\;\max}\left\lbrack {M \times {PF}_{M}} \right\rbrack}.}$